Although metric learning has shown its advantages in face recognition [1]} and person re-identification (re-ID) [2]},
learning discriminative features with deep metric learning for face forgery detection is more or less neglected. Center loss [3]} and triplet loss [1]}
are the two most relevant metric learning methods to our work.
Center loss [3]} is designed to learn a center for features of each class and drive features of the same class closer to their corresponding center.
Obviously, one disadvantage of center loss is that it ignores inter-class separability.
Triplet loss [1]} encourages features of data points with the same identity to get closer than those with different identities.
However, triplet loss may suffer from the problem of time-consuming mining of hard triplets and dramatic data expansion.
Kumar \(\)  [7]} utilizes the network with the supervision of triplet loss to detect face forgery.
But triplet loss performs poorly on the imagenet pre-trained backbone. Two-branch [8]} proposes a novel loss which compresses the variability of natural faces and pushes away the manipulated faces.
But its motivation comes from anomaly detection and the approach is very different from our SCL in many aspects.
For example, our center point is updatable, while the center point of the two-branch is fixed. Additionally,
two-branch constrains the absolute distance from all samples to the center point,
whereas our SCL constrains the relative distance between natural and manipulated samples to the center point.
